Research

Snow is an important part of the water cycle, with over 1 billion people dependent on melt for their water supply. However, we know very little about the global distribution of snow, nor whether it has changed over time. Current methods of estimating snow mass from satellite observations use techniques from the late 1980s, have some poor assumptions behind them, and consequently large errors associated with the measurements. My research aims are to develop algorithms to improve global snow mass and soil moisture estimates from satellite data at microwave wavelengths. This should lead to better water management and risk assessment for flooding and drought conditions.

Scattering of electromagnetic radiation is hugely sensitive to the size of the snow crystals, so we need to know this well in order to retrieve snow mass from the satellite data. Near-infrared reflectance, and physically-based models of the snow will give us an idea of how large the snow crystals are, to help with snow mass retrievals. Remote sensing of the soil moisture detects water content of the top few centimetres of soil, so we need to use computer simulations of water movement to relate the surface soil moisture to the water contained deeper in the soil.

We will use data assimilation techniques to blend physically-based models with passive microwave and other satellite data. Once this system has been developed, we can use it to monitor components of the water cycle, as well as apply it to a 30+ year dataset of observations to examine whether the snow mass has changed with the climate.